Claude Opus 4.7 (Adaptive) vs DeepSeek V3.2

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

Claude Opus 4.7 (Adaptive) is clearly ahead on the provisional aggregate, 90 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Claude Opus 4.7 (Adaptive)'s sharpest advantage is in coding, where it averages 72.9 against 60.9.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 59.5x on output cost alone. Claude Opus 4.7 (Adaptive) is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.